Machine learning (ml) model for participants

ABSTRACT

Provided is a method that includes identifying, by a processor in real time, a trigger event initiated by at least one participant of the meeting. The trigger event is indicative of at least a reference to meeting metadata associated with the meeting. The meeting data associated with at least one participant is recorded for a determined duration to generate meeting snippet based on identification of the trigger event. Further, the method includes training a machine learning (ML) model associated with the at least one participant based on the meeting snippet associated with the at least one participant. Additionally, the method includes generating one or more meeting recommendations by utilizing the trained ML model, wherein the one or more meeting recommendations include meeting metadata for another meeting.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This Application makes reference to, claims priority to, and claimsbenefit from U.S. Provisional Application Ser. No. 63/028,123, which wasfiled on May 21, 2020.

The above referenced Application is hereby incorporated herein byreference in its entirety.

TECHNICAL FIELD

The presently disclosed embodiments are related, in general, to ameeting. More particularly, the presently disclosed embodiments arerelated to a ML model for participants of the meeting.

BACKGROUND

Meetings, conducted over a communication network, involve participantsjoining the meeting through computing devices connected to thecommunication network. In some examples, plurality of participants ofthe meeting may generate meeting data during a course of the meeting.Some examples of the meeting data may include, but not limited to, audiocontent which may include a participant's voice/audio, video contentwhich may include participant's video and/or other videos, meeting notesinput by the plurality of participants, presentation content, and/or thelike. In some examples, the meeting data may be utilized to predictfuture meeting recommendations for the plurality of the participants.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of described systems with some aspects of the presentdisclosure, as set forth in the remainder of the present application andwith reference to the drawings.

SUMMARY

A system and method to generate a ML model for participants is providedsubstantially as shown in, and/or described in connection with, at leastone of the figures, as set forth more completely in the claims.

These and other features and advantages of the present disclosure may beappreciated from a review of the following detailed description of thepresent disclosure, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram that illustrates a system environment fortraining a ML model, in accordance with an embodiment of the disclosure;

FIG. 2 is a block diagram of a central server, in accordance with anembodiment of the disclosure;

FIG. 3 is a diagram that illustrates an example meeting transcript, inaccordance with an embodiment of the disclosure;

FIG. 4 is a diagram that illustrates an exemplary scenario of themeeting, in accordance with an embodiment of the disclosure;

FIG. 5 is a diagram of another exemplary scenario illustratinggeneration of the one or more meeting recommendations, in accordancewith an embodiment of the disclosure; and

FIG. 6 is a flowchart illustrating a method for training the ML model,in accordance with an embodiment of the disclosure;

FIG. 7 is a flowchart illustrating another method for training the MLmodel, in accordance with an embodiment of the disclosure; and

FIG. 8 is a flowchart illustrating a method for generating one or moremeeting recommendations, in accordance with an embodiment of thedisclosure.

DETAILED DESCRIPTION

The illustrated embodiments describe a method that includes identifying,by a processor in real time, a trigger event initiated by at least oneparticipant of the meeting. The trigger event is indicative of at leasta reference to meeting metadata associated with the meeting. The meetingdata associated with at least one participant is recorded for adetermined duration to generate meeting snippets based on identificationof the trigger event. Further, the method includes training a machinelearning (ML) model associated with the at least one participant basedon the meeting snippet associated with the at least one participant.Additionally, the method includes generating one or more meetingrecommendations by utilizing the trained ML model, wherein the one ormore meeting recommendations include meeting metadata and/or meetingdata for one or more meetings.

The various embodiments describe a central server comprising a memorydevice that stores a set of instructions. Further, the central serverincludes a processor communicatively coupled to the memory device,wherein the processor is configured to identify, in real time, a triggerevent initiated by at least one participant of the meeting, wherein thetrigger event is indicative of at least a reference to meeting metadataassociated with the meeting. The processor is further configured torecord meeting data associated with the at least one participant of themeeting for a determined duration to generate a meeting snippet based onthe identification of the trigger event. Furthermore, the processor isconfigured to train a machine learning (ML) model associated with the atleast one participant based on the meeting snippet associated with theat least one participant. Additionally, the processor is configured togenerate one or more meeting recommendations by utilizing the trained MLmodel, wherein the one or more meeting recommendations include meetingmetadata for another meeting.

The various embodiments describe a non-transitory computer-readablemedium having stored thereon, computer-readable instructions, which whenexecuted by a computer, causes a processor in the computer to executeoperations. The operations include identifying, in real time, a triggerevent initiated by at least one participant of the meeting, wherein thetrigger event is indicative of at least a reference to meeting metadataassociated with the meeting. The operations further includes recording,meeting data associated with the at least one participant of the meetingfor a determined duration to generate a meeting snippet, wherein therecording is based on the identified trigger. Additionally, theoperations include training a machine learning (ML) model associatedwith the at least one participant based on the meeting snippetassociated with the at least one participant. The operations furtherinclude generating one or more meeting recommendations by utilizing thetrained ML model, wherein the one or more meeting recommendationsinclude meeting metadata for another meeting.

FIG. 1 is a block diagram that illustrates a system environment fortraining a ML model, in accordance with an embodiment of the disclosure.Referring to FIG. 1, there is shown a system environment 100, whichincludes a central server 102, one or more computing devices 104 a, 104b, and 104 c collectively referenced as computing devices 104, and acommunication network 106. The central server 102 and the computingdevices 104 may be communicatively coupled with each other through thecommunication network 106.

The central server 102 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to create a meetingsession through which the computing devices 104 may communicate witheach other. For example, the computing devices 104, may share content(referred to as meeting data) amongst each other via the meetingsession. For example, the central server 102 may receive the meetingdata from each of the computing devices 104. Thereafter, the centralserver 102 may be configured to monitor the meeting data received fromeach of the computing devices 104. The monitoring of the meeting datamay comprise identifying a trigger event during the meeting. The centralserver 102 may be configured to capture a plurality of meeting snippetsfor each of the plurality of participants based on the identification ofthe trigger event. Additionally, or alternatively, the central server102 may be configured to train a Machine Learning (ML) model for each ofthe plurality of participants based on the plurality of meetingsnippets. In an alternative embodiment, the central server 102 may beconfigured to train the ML model for each of the plurality ofparticipants, directly, based on the meeting data received from the eachof the computing devices 104. Further, the central server 102 may beconfigured to utilize the ML model to generate one or more meetingrecommendations for each of the plurality of participants. Examples ofthe central server 102 may include, but are not limited to, a personalcomputer, a laptop, a personal digital assistant (PDA), a mobile device,a tablet, a computing device coupled to the computing devices 104 over alocal network, an edge computing device, a cloud server, or any othercomputing device. Notwithstanding, the disclosure may not be so limitedand other embodiments may be included without limiting the scope of thedisclosure.

The computing devices 104 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to connect to the meetingsession, created by the central server 102. In an exemplary embodiment,the computing devices 104 may be associated with the plurality ofparticipants of the meeting. The plurality of participants may provideone or more inputs during the meeting that may cause the computingdevices 104 to generate the meeting data during the meeting. In anexemplary embodiment, the meeting data may correspond to the contentshared amongst the computing devices 104 during the meeting. In someexamples, the meeting data may comprise, but are not limited to, audiocontent that is generated by the plurality of participants as theplurality of participants speak during the meeting, video content thatmay include video feed of the plurality of participants, meeting notesinput by the plurality of participants during the meeting, presentationcontent, screen sharing content, file sharing content and/or any othercontent shared during the meeting. In some examples, the computingdevices 104 may be configured to transmit the meeting data to thecentral server 102. Additionally, or alternatively, the computingdevices 104 may be configured to receive an input, indicative of thetrigger event, from the plurality of participants. Upon receiving theinput, the computing devices 104 may be configured to transmit the inputto the central server 102. Examples of the computing devices 104 mayinclude, but are not limited to, a personal computer, a laptop, apersonal digital assistant (PDA), a mobile device, a tablet, or anyother computing device.

In an embodiment, the communication network 106 may include acommunication medium through which each of the computing devices 104associated with the plurality of participants may communicate with eachother and/or with the central server 102. Such a communication may beperformed, in accordance with various wired and wireless communicationprotocols. Examples of such wired and wireless communication protocolsinclude, but are not limited to, Transmission Control Protocol andInternet Protocol (TCP/IP), User Datagram Protocol (UDP), HypertextTransfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE,infrared (IR), IEEE 802.11, 802.16, 2G, 3G, 4G, 5G, 6G cellularcommunication protocols, and/or Bluetooth (BT) communication protocols.The communication network 106 may include, but is not limited to, theInternet, a cloud network, a Wireless Fidelity (Wi-Fi) network, aWireless Local Area Network (WLAN), a Local Area Network (LAN), atelephone line (POTS), and/or a Metropolitan Area Network (MAN).

In operation, the central server 102 may receive a request, from acomputing device 104 a, to generate the meeting session for a meeting.In an exemplary embodiment, the request may include meeting metadataassociated with the meeting that is to be scheduled. In an exemplaryembodiment, the meeting metadata may include, but is not limited to, anagenda of the meeting, one or more topics to be discussed during themeeting, a time duration of the meeting, a schedule of the meeting,meeting notes carried forward from previous meetings, and/or the like.Upon receiving the request, the central server 102 may create themeeting session. In an exemplary embodiment, the meeting session maycorrespond to a communication session that allows the computing devices104 to communicate with each other. The meeting session may share uniquekeys (public and private keys) with the computing devices 104, whichallows the computing devices 104 to communicate with each other. In someexamples, the unique keys corresponding to the meeting session mayensure that any other computing devices (other than the computingdevices 104) are not allowed to join the meeting session. Additionally,or alternatively, the central server 102 may send a notification to thecomputing devices 104 pertaining to the scheduled meeting. Thenotification may include the details of the meeting session. Forexample, the central server 102 may transmit the unique keys and/or themeeting metadata to the computing devices 104.

The computing devices 104 may join the meeting through the meetingsession. In an exemplary embodiment, the plurality of participantsassociated with the computing devices 104 may cause the computingdevices 104 to join the meeting session. In an exemplary embodiment,joining the meeting session has been interchangeably referred to asjoining the meeting. Thereafter, the plurality of participantsassociated with the computing devices 104 may cause the computingdevices 104 to share content amongst each other. For instance, theplurality of participants may provide input to the computing devices 104to cause the computing devices 104 to share the content amongst eachother. For example, the plurality of participants may speak during themeeting. The computing devices 104 may capture voice of the plurality ofparticipants through one or more microphones to generate audio content.Further, the computing devices 104 may transmit the audio content overthe communication network 106 (i.e., meeting session). Additionally, oralternatively, the plurality of participants may share respective videofeeds amongst each other by utilizing image capturing device (e.g.,camera) associated with the computing devices 104. Additionally, oralternatively, a participant of the plurality of participants maypresent content saved on the computing device (for example, thecomputing device 104 a) through screen sharing capability. For example,the participant may present content to other participants (of theplurality of participants) through the power point presentationapplication installed on the computing device 104 a. In some examples,the participant may share content through other applications installedon the computing device 104 a. For example, the participant may sharecontent through the word processor application installed on thecomputing device 104 a. Additionally, or alternatively, the participantmay take meeting notes during the meeting. In an exemplary embodiment,the meeting data may include the audio content, the video content, themeeting notes, and/or the screen sharing content (e.g., throughapplications installed on the computing device 104 a). Accordingly, insome examples, the computing device 104 a may generate the meeting dataduring the meeting. Similarly, other computing devices 104 b and 104 cmay also generate the meeting data during the meeting. Additionally, oralternatively, the computing devices 104 may transmit the meeting datato the central server 102 over the meeting session. In an exemplaryembodiment, the computing devices 104 may transmit the meeting data innear real time. To this end, the computing devices 104 may be configuredto transmit the meeting data as and when the computing devices 104generate the meeting data.

In an exemplary embodiment, the central server 102 may receive themeeting data from each of the computing devices 104. Thereafter, thecentral server 102 may be configured to utilize the meeting data,received from each of the computing devices 104, to train a ML model foreach of the plurality of participants. For example, the central server102 receives the meeting data from the computing device 104 a,associated with the participant-1. Further, the central server 102receives the meeting data from the computing device 104 b, associatedwith the participant-2. Accordingly, the central server 102 may train aML model for the participant-1 based on meeting data received from thecomputing device 104 a. Additionally, the central server 102 may trainanother ML model for the participant-2 based on the meeting datareceived from the computing device 104 b. Accordingly, the centralserver 102 may be configured to train the ML model for each of theplurality of participants.

In some examples, the scope of the disclosure is not limited to thecentral server 102 utilizing the complete meeting data to train the MLmodel for each of the plurality of participants. In some examples, thecentral server 102 may be configured to train the ML model based on aportion of the meeting data received from the computing device 104 a. Insuch an embodiment, prior to training the ML model, the central server102 may compare the meeting data (received from each of the computingdevices 104) with the meeting metadata to identify a trigger event inthe meeting data. For example, the central server 102 may compare themeeting data received from the computing device 104 a with the meetingmetadata to identify the trigger event initiated by the participantassociated with the computing device 104 a. In an exemplary embodiment,the trigger event may be indicative of a timestamp at which theparticipant discussed or referred to a topic corresponding to themeeting metadata. For example, the participant discussed a topicmentioned in the agenda of the meeting.

Based on the identification of the trigger event, the central server 102may generate a meeting snippet by recording the meeting data, receivedfrom a computing device (e.g., computing device 104 a) for a determinedduration. In an example embodiment, the central server 102 may beconfigured to associate the meeting snippet with the participantassociated with the computing device (e.g., computing device 104 a).Similarly, during the meeting, the central server 102 may be configuredto generate a plurality of meeting snippets associated with each of theplurality of participants. Thereafter, the central server 102 may beconfigured to train the ML model for each of the plurality ofparticipants based on the plurality of meeting snippets associated witheach of the plurality of participants.

In exemplary embodiment, the central server 102 may be configured toutilize the ML model to generate one or more meeting recommendations foreach of the plurality of participants. In an example embodiment, the oneor more meeting recommendations may include, but are not limited to,suggesting meeting metadata for another meeting to be scheduled with theplurality of participants.

FIG. 2 is a block diagram of the central server, in accordance with anembodiment of the disclosure. Referring to FIG. 2, there is shown acentral server 102 comprises a processor 202, a non-transitory computerreadable medium 203, a memory device 204, a transceiver 206, a meetingdata monitoring unit 208, a trigger event identification unit 210, arecording unit 212, and a training unit 214, and a recommendation unit216.

The processor 202 may be embodied as one or more microprocessors withaccompanying digital signal processor(s), one or more processor(s)without an accompanying digital signal processor, one or morecoprocessors, one or more multi-core processors, one or morecontrollers, processing circuitry, one or more computers, various otherprocessing elements including integrated circuits such as, for example,an application specific integrated circuit (ASIC) or field programmablegate array (FPGA), or some combination thereof.

Accordingly, although illustrated in FIG. 2 as a single controller, inan exemplary embodiment, the processor 202 may include a plurality ofprocessors and signal processing modules. The plurality of processorsmay be embodied on a single electronic device or may be distributedacross a plurality of electronic devices collectively configured tofunction as the circuitry of the central server 102. The plurality ofprocessors may be in communication with each other and may becollectively configured to perform one or more functionalities of thecircuitry of the central server 102, as described herein. In anexemplary embodiment, the processor 202 may be configured to executeinstructions stored in the memory device 204 or otherwise accessible tothe processor 202. These instructions, when executed by the processor202, may cause the circuitry of the central server 102 to perform one ormore of the functionalities, as described herein.

Whether configured by hardware, firmware/software methods, or by acombination thereof, the processor 202 may include an entity capable ofperforming operations according to embodiments of the present disclosurewhile configured accordingly. Thus, for example, when the processor 202is embodied as an ASIC, FPGA or the like, the processor 202 may includespecifically configured hardware for conducting one or more operationsdescribed herein. Alternatively, as another example, when the processor202 is embodied as an executor of instructions, such as may be stored inthe memory device 204, the instructions may specifically configure theprocessor 202 to perform one or more algorithms and operations describedherein.

Thus, the processor 202 used herein may refer to a programmablemicroprocessor, microcomputer or multiple processor chip or chips thatmay be configured by software instructions (applications) to perform avariety of functions, including the functions of the various embodimentsdescribed above. In some devices, multiple processors may be providedthat may be dedicated to wireless communication functions and oneprocessor may be dedicated to running other applications. Softwareapplications may be stored in the internal memory before they areaccessed and loaded into the processors. The processors may includeinternal memory sufficient to store the application softwareinstructions. In many devices, the internal memory may be a volatile ornon-volatile memory, such as flash memory, or a mixture of both. Thememory can also be located internal to another computing resource (e.g.,enabling computer readable instructions to be downloaded over theInternet or another wired or wireless connection).

The non-transitory computer readable medium 203 may include any tangibleor non-transitory storage media or memory media such as electronic,magnetic, or optical media—e.g., disk or CD/DVD-ROM coupled to processor202.

The memory device 204 may include suitable logic, circuitry, and/orinterfaces that are adapted to store a set of instructions that isexecutable by the processor 202 to perform predetermined operations.Some of the commonly known memory implementations include, but are notlimited to, a hard disk, random access memory, cache memory, read-onlymemory (ROM), erasable programmable read-only memory (EPROM) &electrically erasable programmable read-only memory (EEPROM), flashmemory, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, a compact disc read only memory(CD-ROM), digital versatile disc read only memory (DVD-ROM), an opticaldisc, circuitry configured to store information, or some combinationthereof. In an exemplary embodiment, the memory device 204 may beintegrated with the processor 202 on a single chip, without departingfrom the scope of the disclosure.

The transceiver 206 may correspond to a communication interface that mayfacilitate transmission and reception of messages and data to and fromvarious devices (e.g., computing devices 104). Examples of thetransceiver 206 may include, but are not limited to, an antenna, anEthernet port, a USB port, a serial port, or any other port that can beadapted to receive and transmit data. The transceiver 206 transmits andreceives data and/or messages in accordance with the variouscommunication protocols, such as, Bluetooth®, Infra-Red, I2C, TCP/IP,UDP, and 2G, 3G, 4G or 5G communication protocols.

The meeting data monitoring unit 208 may comprise suitable logic,circuitry, interfaces, and/or code that may configure the central server102 to receive the meeting data from each of the computing devices 104.In an exemplary embodiment, the meeting data monitoring unit 208 may beconfigured to generate a transcript from the meeting data using one ormore known techniques. Some examples of the one or more known techniquesmay include Speech to Text (STT), Optical character Recognition (OCR),and/or the like. In an example embodiment, the meeting data monitoringunit 208 may be configured to individually generate transcript for themeeting data received from each of the computing devices 104.Additionally, or alternatively, the meeting data monitoring unit 208 maybe configured to timestamp the transcript, received from each of thecomputing devices 104, in accordance with a time instant at which thecentral server 102 received the meeting data (from which the transcriptwas generated). The meeting data monitoring unit 208 may be implementedusing Field Programmable Gate array (FPGA) and/or Application SpecificIntegrated Circuit (ASIC).

The trigger event identification unit 210 may comprise suitable logic,circuitry, interfaces, and/or code that may configure the central server102 to compare the transcript of the meeting data with the meetingmetadata. Based on the comparison between the meeting metadata and thetranscript of the meeting data, the trigger identification unit 210 maybe configured to identify the trigger event. In an example embodiment,the trigger event identification unit 210 may be configured toindividually identify the trigger event in the meeting data receivedfrom each of the computing devices 104. The trigger event identificationunit 210 may be configured to associate the trigger event with atimestamp. In an example embodiment, the timestamp may correspond to atime instant at which the at least one participant mentioned or referredto the meeting metadata. In an exemplary embodiment, the trigger eventidentification unit 210 may be configured to receive an input from acomputing device (e.g., the computing device 104 a) of the computingdevices 104. The trigger identification unit 210 may identify thereceived input as the trigger event for the computing device 104 a. Thetrigger event identification unit 210 may be implemented using FieldProgrammable Gate array (FPGA) and/or Application Specific IntegratedCircuit (ASIC).

The recording unit 212 may comprise suitable logic, circuitry,interfaces, and/or code that may configure the central server 102 togenerate a meeting snippet based on the identification of the triggerevent. In an exemplary embodiment, the recording unit 212 may beconfigured to record the meeting data (in which the trigger event isidentified) for a determined duration in order to generate the meetingsnippet. For example, the recording unit 212 may be configured to recordthe meeting data, received from the computing device 104 a, to generatemeeting snippet. In an exemplary embodiment, the recording unit 212 maybe configured to generate a plurality of meeting snippets by recordingthe meeting data received from a computing device (e.g., 104 a). Therecording unit 212 may be implemented using Field Programmable Gatearray (FPGA) and/or Application Specific Integrated Circuit (ASIC).

The training unit 214 may comprise suitable logic, circuitry,interfaces, and/or code that may configure the central server 102 totrain the ML model for each of the plurality of participants based onthe meeting data received from the respective computing devices 104. Forexample, the training unit 214 may be configured to train the ML modelfor the participant-1 based on the meeting data received from thecomputing device 104 a (being used by the participant-1). Similarly, thetraining unit 214 may be configured to train another ML model for theparticipant-2 based on the meeting data received from the computingdevice 104 b (being used by the participant-2). In another example, thetraining unit 214 may be configured to train the ML model for each ofthe plurality of participants based on the plurality of meetingsnippets. Additionally, or alternatively, the training unit 214 may beconfigured to train the ML model based on other information obtainedfrom other sources such as, but not limited to, one or more projecttracking tools, and/or meeting metadata. The training unit 214 may beimplemented using Field Programmable Gate array (FPGA) and/orApplication Specific Integrated Circuit (ASIC).

The recommendation unit 216 may comprise suitable logic, circuitry,interfaces, and/or code that may configure the central server 102 togenerate the one or more meeting recommendations for each of theplurality of participants. The recommendation unit 216 may beimplemented using Field Programmable Gate array (FPGA) and/orApplication Specific Integrated Circuit (ASIC).

In operation, the processor 202 may receive the request to schedule themeeting from at least one computing device 104 a of the computingdevices 104. In an exemplary embodiment, the request to schedule themeeting includes meeting metadata. As discussed, the meeting metadataincludes the agenda of the meeting, the one or more topics to bediscussed during the meeting, the time duration of the meeting, theschedule of the meeting, the meeting notes carried from previousmeetings, and/or the like. Following table illustrates an examplemeeting metadata:

TABLE 1 Example meeting metadata Meeting notes One or more Time Scheduleof from previous Agenda topics duration the meeting meetings Todiscuss 1. Layout 1 hour 15^(th) Nov. 1. UI to design of the 2. Fieldsto 2020; 9 include feature 1, User be displayed PM to 10 PM feature 2interface in UI 2. Feature 1 (UI) 3. Current defined as a status ofportion depicting project participants 3. Feature 2 depicting chat box

In an exemplary embodiment, the processor 202 may be configured to storethe meeting metadata in the memory device 204. Additionally, based onreceiving the request to schedule the meeting, the processor 202 may beconfigured to create the meeting session. As discussed, the meetingsession corresponds to a communication session that allows the computingdevices 104 to connect to the central server 102. Further, the meetingsession allows the computing devices 104 to communicate amongst eachother. For example, over the meeting session, the computing devices 104may share content (e.g., audio content and/or video content) amongsteach other. In an exemplary embodiment, the processor 202 may beconfigured to transmit a message to each of the computing devices 104comprising the details of the meeting session. For example, the messagemay include a link to connect to the meeting session.

At the scheduled time, the plurality of participants may cause therespective computing devices 104 to join the meeting session. Forexample, the participant may click on the link (received in the messagefrom the central server 102) to cause the computing devices 104 to jointhe meeting session. Based on the computing devices 104 joining themeeting session, the central server 102 may transmit a User Interface(UI) to each of the computing devices 104. In an exemplary embodiment,the UI may allow the plurality of participants to access one or morefeatures. For example, the UI may allow the plurality of participants toshare audio content and/or video content. To this end, the UI mayprovide control to the plurality of participants to enable/disable animage capturing device and/or an audio capturing device in the computingdevices 104. Additionally, or alternatively, the UI may enable theplurality of participants to share other content. For example, the UImay provide a feature to the plurality of participants that would allowthe plurality of participants to cause the computing devices 104 toshare content/applications being displayed on a display deviceassociated with the computing devices 104. For instance, through the UI,the plurality of participants may cause the computing devices 104 toshare a power point presentation being displayed on the computingdevices 104. Additionally, or alternatively, the UI may present a notefeature to the plurality of participants on respective computing devices104. The notes feature may enable the plurality of participants to inputnotes or keep track of important points discussed during the meeting.For example, the notes feature of the UI may correspond to a space onthe UI in which the plurality of participants may input text for his/herreference. Further, the text input by the plurality of participants maycorrespond to the notes taken by the plurality of participants duringthe meeting. Additionally, or alternatively, the computing devices 104may be configured to transmit the text input by the plurality ofparticipants to the central server 102. Further, in one embodiment, thecentral server 102 may be configured to share the text input by theplurality of participants amongst each of the computing devices 104. Inan alternative embodiment, the central server 102 may not share the textinput by the plurality of participants amongst each of the computingdevices 104.

The plurality of participants may utilize the one or more featurespresented on the UI to interact and/or share content amongst each other.Accordingly, each of the computing devices 104 may generate meeting dataduring the meeting. As discussed, the meeting data may include, but isnot limited to, the audio content generated by the plurality ofparticipants as the plurality of participants speak during the meeting,the video content includes video feed of the plurality of participants,the meeting notes input by the plurality of participants during themeeting, the presentation content, the screen sharing content, the filesharing content and/or any other content shared during the meeting. Tothis end, in an exemplary embodiment, the processor 202 may receive themeeting data from each of the computing devices 104 in real time.

In some examples, since the computing devices 104 generate the meetingdata based on the input provided by the plurality of participants.Accordingly, the meeting data received from each of the computingdevices 104 are associated with the respective participants using thecomputing devices 104. For example, the meeting data received from thecomputing device 104 a is associated with the participant-1 using thecomputing device 104 a. For the purpose of brevity, the foregoingdescription has been described in conjunction with the meeting datareceived from the computing device 104 a. However, those skilled in theart would appreciate that the foregoing description is also applicableon the meeting data received from the other computing devices 104.

In an exemplary embodiment, the meeting data monitoring unit 208 may beconfigured to generate, in real time, a transcript of the meeting datareceived from the computing device 104 a. For example, the meeting datamonitoring unit 208 may be configured to convert the audio content(received from computing devices 104) to text using known Speech to Text(STT) techniques. The text (obtained from the audio content) mayconstitute the transcript. In another example, the meeting datamonitoring unit 208 may be configured to generate the transcript fromthe video content. For instance, the meeting data monitoring unit 208may perform optical character recognition (OCR) on the video content togenerate the transcript. In yet another example, the meeting datamonitoring unit 208 may be configured to consider the meeting notes(input by the participant associated with the computing device 104 a) asthe transcript. In yet another example, the meeting data monitoring unit208 may be configured to perform OCR on the content shared via thescreen sharing feature to generate the transcript. Additionally, oralternatively, the meeting data monitoring unit 208 may be configured totimestamp the transcript in accordance with a time instant of thereception of the meeting data from the computing device 104 a. Forexample, the processor 202 receives the meeting data at time instant T₁.To this end, the meeting data monitoring unit 208 may generate thetranscript from the meeting data received at the time instant T₁, andmay timestamp the transcript with time instant T₁. An example thetranscript is further illustrated and described in FIG. 3. Similarly,during the meeting the meeting data monitoring unit 208 may beconfigured to generate multiple transcripts of the meeting data receivedfrom the computing device 104 a based on the time instant at which thecentral server 102 receives the corresponding meeting data. For example,the meeting data monitoring unit 208 may generate another transcript atthe time instant T₂ based on the meeting data received at the timeinstant T₂. To this end, the meeting data monitoring unit 208 may beconfigured to generate the transcripts as and when the central server102 receives the meeting data from the computing device 104 a.

Additionally, or alternatively, the meeting data monitoring unit 208 maybe configured to include the meeting metadata (generated duringscheduling the meeting) in the transcript. Additionally, oralternatively, the meeting data monitoring unit 208 may be configured toretrieve task metadata associated with one or more tasks assigned to theparticipant-1 from the one or more project tracking tools. Someexamples, of the project tracking tools may include, but are not limitedto, Salesforce®, Era®, and/or the like. In an exemplary embodiment, thetask metadata may include, but not limited to, tasks description, taskoutcome, tools to be used to complete the task, planned completion dateassociated with the task, and/or a current status of the task. In someexamples, the participant-1 may be working on more than one project inparallel. Accordingly, the participant-1 may be assigned with multipletasks. The task metadata associated with such multiple tasks is usuallystored on the one or more project tracking tools. The meeting datamonitoring unit 208 may be configured to retrieve the task metadataassociated with the one or more tasks, assigned to the participant 1,from the project tracking tools. In an alternative embodiment, themeeting data monitoring unit 208 may be configured to retrieve the taskmetadata associated with a set of tasks, of the one or more tasksassigned to the participant 1, that are relevant to the meeting. Forexample, the meeting data monitoring unit 208 may be configured toretrieve the task metadata associated with the set of tasks based on themeeting metadata. To this end, the meeting data monitoring unit 208 maybe configured to query the Application Interface (API) of the one ormore project tracking tools using the meeting metadata to retrieve thetask metadata associated with the set of tasks. For example, the meetingmetadata includes the agenda UI design. Accordingly, the meeting datamonitoring unit 208 may be configured to retrieve the task metadataassociated with the set of tasks assigned to the participant-1pertaining to the UI design. Further, the meeting data monitoring unit208 may be configured to add the task metadata in the transcript.

FIG. 3 is a diagram that illustrates an example meeting transcript, inaccordance with an embodiment of the disclosure. Referring to FIG. 3,there is shown a meeting transcript 300 that includes a transcript“agendal: to create UI” (depicted by 302) received at the time instantT₁ (depicted by 304) from the computing device 104 a. Similarly, themeeting transcript 300 includes another transcript “UI to includefeature 1 and feature 2” (depicted by 306) received at the time instantT₂ (depicted by 308) from the computing device 104 a. Additionally, themeeting transcript 300 includes the task metadata 310 associated withthe set of tasks assigned to the participant-1 associated with thecomputing device 104 a. Additionally, or alternatively, the meetingtranscript 300 includes the meeting metadata 312.

In an exemplary embodiment, the training unit 214 may be configured totrain a ML model for the participant-1 associated with the computingdevice 104 a based on the transcript (generated from the meeting datareceived from the computing device 104 a, the task metadata associatedwith the set of tasks assigned to the participant-1, and meetingmetadata). In some examples, the ML model may be indicative of a profileof the participant 1. In an exemplary embodiment, the profile of aparticipant may be deterministic of one or more topics which arerelevant and/or of interest to the participant. Additionally, oralternatively, the profile may be indicative of one or more skills ofthe participant 1.

To train the ML model, the training unit 214 may be configured to removeunwanted words and/or phrases, from the transcript to generate a cleantranscript. Such unwanted words and/or phrases may be referred to asstop words. In some examples, the stop words may include words that areinsignificant and do not add meaning to the transcript. Some examples ofthe stop words may include, but are not limited to, “is”, “are”, “and”“at least”, and/or the like. Thereafter, in some examples, the trainingunit 214 may be configured to identify n-grams in the clean transcript,where n-grams corresponds to combination of two or more words in theclean transcript that are used in conjunction, in the transcript. Forexample, the term “user” and “interface” are often used together.Accordingly, the training unit 214 may be configured to identify theterm “user interface” as an n-gram. In an exemplary embodiment, thetraining unit 214 may be configured to add the identified n-gram to theclean transcript to create a training corpus.

Thereafter, the training unit 214 may be configured to train the MLmodel using the training corpus. In some examples, training the ML modelusing the training corpus may include converting the words in trainingcorpus in one or more vectors. Thereafter, the training unit 214 may beconfigured to train a neural network using the one or more vectors. Thetrained neural network corresponds to the ML model. Those skilled in theart would appreciate that scope of the disclosure is not limited tousing the neural network as the ML model. In an exemplary embodiment,the ML model may be realized using other techniques such as, but notlimited to, logistic regression, Bayesian regression, random forestregression, and/or the like.

In an exemplary embodiment, as discussed, the ML model is associatedwith the participant 1. Similarly, the training unit 214 may beconfigured to train other ML models for other participants.

In some examples, the scope of the disclosure is not limited to thetraining the ML model using the training corpus generated from themeeting data. In an exemplary embodiment, the training unit 214 may beconfigured to generate training corpus based on an identification of atrigger event in the meeting data. To this end, in an exemplaryembodiment, the trigger event identification unit 210 may be configuredto compare the meeting metadata and the transcript. In an exemplaryembodiment, the trigger event identification unit 210 may compare thetranscript at each timestamp (in the meeting transcript) with themeeting metadata using one or more known text comparison techniques.Some examples of the text comparison techniques may include, but not arelimited to, Cosine Similarity, Euclidean distance, Pearson coefficientand/or the like. In order to utilize the text comparison techniques, thetrigger event identification unit 210 may be configured to convert thetranscript at each timestamp into a transcript vector using one or moreknown transformation techniques such as, but not limited to, termfrequency—inverse document frequency (TF-IDF), Wor2Vec, and/or the like.In an exemplary embodiment, the transcript vector may correspond to anarray of integers, in which each integer corresponds to a term in thetranscript. Further, the value of the integer may be deterministic ofthe characteristic of the term within the transcript. For example, theinteger may be deterministic of a count of times a term has appeared inthe transcript. Similarly, the trigger event identification unit 210 maybe configured to convert the meeting metadata to a metadata vector.Thereafter, the trigger event identification unit 210 may utilize theone or more text comparison techniques to compare the metadata vectorand the transcript vector and determine a similarity score between themetadata vector and the transcript vector. For example, the triggerevent identification unit 210 may determine a Cosine similarity scorebetween the metadata vector and the transcript vector.

In some embodiments, the trigger event identification unit 210 may beconfigured to determine whether the similarity score is greater than orequal to a similarity score threshold. If the trigger eventidentification unit 210 determines that similarity score is less thanthe similarity score threshold, the trigger event identification unit210 may be configured to determine that the transcript is dissimilarfrom the meeting metadata. However, if the trigger event identificationunit 210 determines that the similarity score is greater than or equalto the similarity score threshold, the trigger event identification unit210 may be configured to determine that the transcript is similar to themeeting metadata. Accordingly, the trigger event identification unit 210may determine that the participant-1 mentioned or presented contentrelated to the meeting metadata. To this end, the trigger eventidentification unit 210 may identify the trigger event.

In some embodiments, the scope of the disclosure is not limited to thetrigger event identification unit 210 identifying the trigger eventbased on the comparison between the meeting data and the meetingmetadata. In an exemplary embodiment, the trigger event identificationunit 210 may be configured to receive an input from a computing device(e.g., 104 a) of the computing devices 104. The input may indicate thata participant may want to record a portion of the meeting for laterreference. For example, during the meeting, the participant may find thediscussion and/or the content being presented to be interesting.Accordingly, in some examples, the participant may provide an input onthe UI to record the portion of the meeting that includes the discussionthat the participant found interesting. In such an embodiment, thecomputing device 104 a may transmit the input (received from theparticipant through UI) to the central server 102. Upon receiving theinput from the computing device 104 a, the trigger event identificationunit 210 may identify the input as the trigger event.

Additionally, or alternatively, the processor 202 may be configured tocategorize the transcript at each timestamp in one or more categories.In an exemplary embodiment, the one or more categories may include anaction category, a schedule category, work status category, and or thelike. In an exemplary embodiment, the action category may correspond toa category that may comprise transcripts which are indicative of anaction item for the plurality of participants. In an exemplaryembodiment, the schedule category may correspond to a category that maycomprise transcripts indicative of schedule of a subsequent meeting. Inyet another embodiment, the work status category may correspond to acategory that may include transcripts indicative of status of a task ora work.

In an exemplary embodiment, the processor 202 may be configured toutilize a classifier to categorize the transcript at each timestamp inthe one or more categories. In some examples, the classifier maycorrespond to a machine learning (ML) model that is capable ofcategorizing the transcript at each timestamp based on the semantics ofthe transcripts. For example, the ML model may be capable oftransforming the transcript into the transcript vector. Thereafter, theML model may be configured to utilize the known classificationtechniques to classify the transcript at each transcript in the one ormore categories. Some examples of the classification techniques mayinclude, but not limited to, naïve bayes classification technique,logistic regression, hierarchal classifier, random forest classifier,and/or the like. In some examples, prior to utilizing the classifier toclassify the transcripts in the one or more categories, the processor202 may be configured to train the classifier based on training data.The training data may include one or more features and one or morelabels. The one or more features may include training transcripts, whilethe one or more labels may include the one or more categories. In thetraining data, each of the transcript is associated with a category ofthe one or more categories. Training the classifier may include theprocessor 202 defining a mathematical relationship between thetranscript vectors and the one or more categories. Thereafter, theprocessor 202 utilizes the classifier to classify the transcript to theone or more categories.

In some examples, the trigger event identification unit 210 may beconfigured to identify the trigger event based on the classification ofthe transcript in the one or more categories. Additionally, oralternatively, the trigger event identification unit 210 may beconfigured to identify the trigger event based on the categorization ofthe transcript in the one or more categories and the reception of theinput from the computing device (e.g., 104 a). Additionally, oralternatively, the trigger event identification unit 210 may beconfigured to identify the trigger event based on the categorization ofthe transcript in the one or more categories and the similarity score.Additionally, or alternatively, the trigger event identification unit210 may be configured to identify the trigger event based on thesimilarity score and the reception of the input from the computingdevice (e.g., 104 a). Additionally, or alternatively, the trigger eventidentification unit 210 may be configured to identify the trigger eventbased on the categorization of the transcript in the one or morecategories, reception of the input from the computing device (e.g., 104a), and the similarity score.

In an exemplary embodiment, based on the identification of the triggerevent, the recording unit 212 may be configured to record the meetingdata received from the computing device 104 a, for the determinedduration. In an exemplary embodiment, a length of the determinedduration may be defined during configuration of the central server 102.Further, the determined duration may be defined based on the timestampassociated with the transcript corresponding to the trigger event (i.e.,the transcript that is similar to the meeting metadata). In an alternateembodiment, the determined duration may be defined based on thetimestamp of the reception of the input from the computing device 104 a.In an exemplary embodiment, the determined duration is defined toinclude a first determined duration chronologically prior to thetimestamp and a second determined duration chronologically after thetimestamp. In some examples, a length of the first determined durationis same as a length of the second determined duration. In anotherexample, the length of the first determined duration is different fromthe length of the second determined duration. For instance, the lengthof the first determined duration is greater than the length of thesecond determined duration. In another instance, the length of thesecond determined duration is greater than the length of the firstdetermined duration.

In view of the foregoing, the recording unit 212 may be configured tocontiguously record the meeting data for first determined duration priorto the timestamp and for the second determined duration after thetimestamp. Accordingly, the recording of the meeting data includes, therecording of the audio content, the video content, the screen sharingcontent, the meeting notes, the presentation content and/or the like,received during the determined duration. In some examples, the recordedmeeting data may correspond to the meeting snippet.

In some examples, the recording unit 212 may be configured to record themeeting data for the determined duration after the timestamp. In anotherexample, the recording unit 212 may be configured to record the meetingdata for the determined duration prior to the timestamp. In an exemplaryembodiment, using the methodology described herein, the recording unit212 may be configured to record a plurality of meeting snippets from themeeting data received from the computing device 104 a. Thereafter, themeeting data monitoring unit 208 may be configured to generate aplurality of transcripts for each of the plurality of meeting snippets.Further, the meeting data monitoring unit 208 may be configured toaggregate the plurality of transcripts to generate a summary transcript.In an exemplary embodiment, the meeting data monitoring unit 208 may beconfigured to aggregate the plurality of transcripts based on thechronological order of the timestamp associated with each of therespective meeting snippets to generate a summary transcript. In someexamples, the summary transcript may capture moments in the meeting inwhich the participant-1 caused the identification of the trigger event.

Additionally, or alternatively, the recording unit 212 may be configuredto record the meeting data received from the other computing devices 104for the determined duration based on the identification of the triggerevent to generate a plurality of additional meeting snippets. Themeeting data monitoring unit 208 may be configured to generateadditional meeting transcripts based on the plurality of additionalmeeting snippets.

In some examples, a computing device 104 c of the computing devices 104may not generate the meeting data. For example, in such an embodiment,the participant associated with the computing device may only belistening to the meeting and may be providing inputs to record meetingsnippets. In such an embodiment, the central server 102 may beconfigured to record the meeting data received from other computingdevices 104 for a determined duration to generate the meeting snippet,based on the reception of the input from the computing device 104 c.Further, the central server 102 may be configured to convert the meetingsnippet to transcript, where the transcript is associated with thecomputing device 104 c. Furthermore, the central server 102 may beconfigured to train the ML model for the participant associated with thecomputing device 104 c based on the transcript obtained from the meetingsnippet.

In an exemplary embodiment, the training unit 214 may be configured togenerate a training corpus from the summary transcript and/or theadditional transcript using the methodology described above. Further,the training unit may be configured to train the ML model using thetraining corpus generated from the summary transcript and/or theadditional transcript. Similarly, the training unit 214 may beconfigured to train other ML models for the other participants. Further,the training unit 214 may be configured to store the ML models, trainedfor each of the plurality of participants, in the memory device 204. Insome examples, where the ML model for a participant in the meeting isalready stored on the memory device 204, the training unit 214 may beconfigured to update the existing ML model. In such an embodiment, thetraining unit 214 may be configured to update the existing ML modelbased on the training corpus generated from the transcript of themeeting data associated with the participant.

In an exemplary embodiment, the processor 202 may be configured toreceive another input from the computing device 104 a (associated withthe participant 1) to schedule another meeting. In some examples, theinput may further include details pertaining to other participants thatthe participant-1 intends to be part of the meeting. In such anembodiment, the processor 202 may be configured to retrieve the ML modelassociated with the participant-1 and the other participants from thememory device 204. Thereafter, the processor 202 may be configured togenerate one or more meeting recommendations for the other meetingsbased on the ML model associated with the participant-1 and the otherparticipants. For example, the processor 202 may be configured todetermine one or more topics that are common to the participant-1 andthe other participants based on the respective ML models. The processor202 may be configured to utilize the one or more topics as the one ormore meeting recommendations.

Further, during the other meeting, the ML model associated with theplurality of participants may enable the central server 102 to capture aplurality of meeting snippets that may be of interest to the pluralityof participants. For example, based on the one or more topics associatedwith each of the plurality of participants (determined from the ML modelassociated with each of the plurality of participants), the centralserver 102 may be configured to identify trigger events during the othermeeting. For example, in such an embodiment, the central server 102 maybe configured to identify (during the other meeting) time instants atwhich the plurality of participants referred to the one or more topics,as the trigger events. Accordingly, based on the identification of thetrigger events, the central server 102 may be configured to record themeeting for the determined duration to generate a plurality of meetingsnippets.

The scope of the disclosure is not limited to capturing the plurality ofsnippets during the meeting. In an exemplary embodiment, the firstprocessor 202 may be configured to capture the plurality of meetingsnippets of one or more non-real time meeting data shared amongst theplurality of participants. The one or more non-real time meeting datamay include meeting data that is shared amongst the plurality ofparticipants outside the meeting. For example, the one or more non-realtime meeting data may include text messages shared amongst the pluralityof participants, the one or more audio messages shared amongst theplurality of participants. In some examples, first processor 202 may beconfigured to record the plurality of meeting snippets of the one ormore non-real time meeting data using similar methodology, as isdescribed above.

FIG. 4 is a diagram that illustrates an exemplary scenario of themeeting, in accordance with an embodiment of the disclosure. Referringto FIG. 4, the exemplary scenario 400 illustrates that each of thecomputing devices 104 generates the meeting data. Additionally, oralternatively, each of the computing devices 104 transmit the meetingdata to the central server 102. The meeting data 402 transmitted by thecomputing device 104 a comprises text corresponding to the audio contentspoken by the participant-1 associated with the computing device 104 a.The text indicates “referring to topic 1, participant 2 will provide thedetails”. Further, the timestamp associated with the meeting data,transmitted by the computing device 104 a, is T₁. At time instant T₂,the computing device 104 b generates the meeting data 404 that includestext obtained from presentation content (by performing OCR). The textindicates “with reference to topic-1, the UI includes feature-1feature-2 and feature-3”. Further, at time instant T2, the exemplaryscenario 400 illustrates that the computing device 104 c transmits aninput 405 to the central server 102.

In an exemplary embodiment, the meeting data monitoring unit 208 appendsthe task metadata 406 associated with the set of tasks assigned to theparticipant-1 to the meeting data received from the computing device 104a. As illustrated, the task metadata for the participant-1 indicates“Design feature of UI” (depicted by 408). Additionally, oralternatively, the meeting data monitoring unit 208 appends the meetingmetadata 410 to the meeting data 402 received from the computing device104 a and the meeting data 404 received from the computing device 104 b.Since the computing device 104 c does not generate the meeting data,based on receiving the input from the computing device 104 c, therecording unit 212 may be configured to record the meeting data 402received from the computing device 104 a and the meeting data 404received from the computing device 104 b for the determined duration togenerate a meeting snippet-1 (depicted by 412) and a meeting snippet-2(depicted by 414), respectively. Further, the meeting data monitoringunit 208 may be configured to consider the meeting snippet-1 (depictedby 412) and the meeting snippet-2 (depicted by 414) as the meeting data416 for the computing device 104 c.

In an exemplary embodiment, the meeting data monitoring unit 208 may beconfigured to generate the transcript 418 from the meeting data 402(received from the computing device 104 a), the transcript 420 from themeeting data 404 (received from the computing device 104 b), and thetranscript 422 from the meeting data 416 associated with the computingdevice 104 c. The transcript 418 includes “Design feature for UI, UIdevelopment, feature 1 of UI is WIP”. The transcript 420 includes “Colorscheme of UI, UI development, feature 2 of UI is complete”. Thetranscript 422 includes “Design feature for UI, UI development, feature1 of UI is WIP, Color scheme of UI, UI development, feature 2 of UI iscomplete”.

The training unit 214 may be configured to generate the trainingcorpuses 424, 426, and 428 based on the transcript 418, the transcript420, and the transcript 422, respectively. The training corpuses 424,426, and 428 are associated with the participant-1, participant-2, andparticipant-3, respectively. Based on the training corpuses 424, 426,and 428, the training unit 214 may be configured to train the ML model-1430, ML model-2 432, and ML model-3 434. The ML model-1 430, ML model-2432, and ML model-3 434 are associated with the participant-1,participant-2, and participant-3, respectively. As discussed, the MLmodel is indicative of one or more topics and/or skills associated witha participant. For example, the ML model-1 430 includes “UI”,“feature-1”, and “C++” as the one or more topics and/or skillsassociated with the participant-1. In another example, the ML model-2432 includes “UI”, “feature-2”, and “Java” as the one or more topicsand/or skills associated with the participant-2.

FIG. 5 is a diagram that illustrates another exemplary scenarioillustrating generation of the one or more meeting recommendations, inaccordance with an embodiment of the disclosure. Referring to FIG. 5,the exemplary scenario 500 includes the ML model-1 430, the ML model-2432, and the ML model-3 434. Further, the exemplary scenario 500illustrates the one or more topics 502, 504, and 506 represented by eachof the ML model-1 430, the ML model-2 432, and the ML model-3 434,respectively. For example, the one or more topics 502 associated withthe ML model-1 432 includes “UI”, “feature-1”, and “C++”. Similarly, theone or more topics 504 associated with the ML model-2 includes “UI”,“feature-2”, and “Java”. Further, the one or more topics 506 associatedwith the ML model-3 includes “UI, feature 1, Color scheme”.

Further, the exemplary scenario 500 illustrates that central server 102receives an input from the computing device 104 a pertaining toscheduling a meeting. The input may further include the detailspertaining to the plurality of participants of the meeting. For example,the details pertaining to the plurality participants includes theparticipant-1 and the participant-2. Thereafter, the recommendation unit216 may be configured to utilize the ML model-1 430 and the ML model-2432 (associated with the participant-1 and the participant-2,respectively) to determine the one or more topics associated with theparticipant-1 and the participant-2. Further, the recommendation unit216 may be configured to determine an intersection between the one ormore topics associated with participant-1 and the one or more topicsassociated with the participant-2 (depicted by 508). For example, therecommendation unit 216 determines that the intersection between the oneor more topics associated with the participant-1 and the participant-2is “UI” (depicted by 510). Accordingly, the recommendation unit 216 maybe configured to generate the meeting recommendation “UI” (depicted by510).

FIG. 6 is a flowchart illustrating a method for training the ML model,in accordance with an embodiment of the disclosure. Referring to FIG. 6,at 602, the meeting data is received from the computing devices 104. Inan exemplary embodiment, the processor 202 may be configured to receivethe meeting data from each of the computing devices 104 during themeeting. At 604, the transcript is created based on the meeting data. Inan exemplary embodiment, the meeting data monitoring unit 208 may beconfigured to transform the meeting data to the transcript. At 606, theML model is trained based on the meeting data. In an exemplaryembodiment, the training unit 208 may be configured to train the MLmodel based on the meeting data. In some examples, the training unit 208may be configured to train the ML model for each of the plurality ofparticipants.

FIG. 7 is a flowchart illustrating another method for training the MLmodel, in accordance with an embodiment of the disclosure. Referring toFIG. 7, at 702, the meeting data is received from the computing devices104. In an exemplary embodiment, the processor 202 may be configured toreceive the meeting data from each of the computing devices 104 duringthe meeting. At 704, a trigger event is identified in the meeting data.In an exemplary embodiment, the trigger event identification unit 210may be configured to identify the trigger event in the meeting data. At706, meeting data is recorded for determined duration. In an exemplaryembodiment, the recording unit 212 may be configured to record themeeting data to generate meeting snippet. At 708, the transcript iscreated based on the meeting snippet. In an exemplary embodiment, themeeting data monitoring unit 208 may be configured to generatetranscript. At 710, the ML model is trained based on the meeting data.In an exemplary embodiment, the training unit 208 may be configured totrain the ML model based on the meeting data.

FIG. 8 is a flowchart 800 illustrating a method for generating one ormore meeting recommendations, in accordance with an embodiment of thedisclosure. Referring to FIG. 8, at 802, an input to schedule a meetingis received from a participant. In an exemplary embodiment, theprocessor 202 may be configured to receive the input. In an exemplaryembodiment, the input includes the details of other participants of themeeting. At 804, the one or more topics associated with each of theparticipants is determined based on respective ML models. In anexemplary embodiment, the recommendation unit 216 may be configured todetermine the one or more topics for each of the participants. At 806,the intersection of the one or more topics associated with each of theparticipants is determined. In an exemplary embodiment, therecommendation unit 216 is configured to determine the intersection. At808, the one or more meeting recommendations are generated. In anexemplary embodiment, the recommendation unit 216 may be configured todetermine the one or more meeting recommendations based on theintersection.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the operations of the various embodiments must beperformed in the order presented. As will be appreciated by one of skillin the art, the operations may be performed in one or more differentorders without departing from the various embodiments of the disclosure

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the aspectsdisclosed herein may include a general purpose processor, a digitalsignal processor (DSP), a special-purpose processor such as anapplication specific integrated circuit (ASIC) or a field programmablegate array (FPGA), a programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor may be a microprocessor, but, in thealternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Alternatively, or in addition, some operations or methodsmay be performed by circuitry that is specific to a given function.

In one or more exemplary embodiments, the functions described herein maybe implemented by special-purpose hardware or a combination of hardwareprogrammed by firmware or other software. In implementations relying onfirmware or other software, the functions may be performed as a resultof execution of one or more instructions stored on one or morenon-transitory computer-readable media and/or one or more non-transitoryprocessor-readable media. These instructions may be embodied by one ormore processor-executable software modules that reside on the one ormore non-transitory computer-readable or processor-readable storagemedia. Non-transitory computer-readable or processor-readable storagemedia may in this regard comprise any storage media that may be accessedby a computer or a processor. By way of example but not limitation, suchnon-transitory computer-readable or processor-readable media may includeRAM, ROM, EEPROM, FLASH memory, disk storage, magnetic storage devices,or the like. Disk storage, as used herein, includes compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray Disc™, or other storage devices that store data magnetically oroptically with lasers. Combinations of the above types of media are alsoincluded within the scope of the terms non-transitory computer-readableand processor-readable media. Additionally, any combination ofinstructions stored on the one or more non-transitory processor-readableor computer-readable media may be referred to herein as a computerprogram product.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of teachings presented in theforegoing descriptions and the associated drawings. Although the figuresonly show certain components of the apparatus and systems describedherein, it is understood that various other components may be used inconjunction with the supply management system. Therefore, it is to beunderstood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, the operations in the method described above may notnecessarily occur in the order depicted in the accompanying diagrams,and in some cases one or more of the operations depicted may occursubstantially simultaneously, or additional operations may be involved.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

What is claimed is:
 1. A method, comprising: identifying, by a processorin real time, a trigger event initiated by at least one participant ofthe meeting, wherein the trigger event is indicative of at least areference to meeting metadata associated with the meeting; recording, bythe processor, meeting data associated with the at least one participantof the meeting for a determined duration to generate a meeting snippet,wherein the recording is based on the identified trigger, wherein themeeting snippet is associated with the at least one participant;training, by the processor, a machine learning (ML) model associatedwith the at least one participant based on the meeting snippetassociated with the at least one participant; and generating, by theprocessor, one or more meeting recommendations by utilizing the trainedML model, wherein the one or more meeting recommendations includemeeting metadata for another meeting.
 2. The method of claim 1, whereinthe meeting data comprises a transcript of the audio generated by the atleast one participant during the meeting, a transcript of the contentshared by the at least one participant during the meeting, and/ormeeting notes input by the at least one participant.
 3. The method ofclaim 1, further comprising training, by the processor, the ML modelbased on the meeting metadata associated with the meeting.
 4. The methodof claim 1, wherein the ML model may be configured to associate the atleast one participant with one or more topics.
 5. The method of claim 1,further comprising generating, by the processor, the one or more meetingrecommendations based on the ML model associated with the otherparticipants of the other meeting.
 6. The method of claim 1, wherein theML model associated with the at least one participant is furtherconfigured to facilitate capture of the meeting data during the othermeeting.
 7. The method of claim 1 further comprising retrieving, by theprocessor, task metadata associated with one or more tasks assigned tothe at least one participant, from one or more project management tools.8. The method of claim 7 further comprising appending, by the processor,the task metadata to the meeting data.
 9. The method of claim 1 furthercomprising training, by the processor, the ML model associated with theat least one participant based on the meeting data associated with theat least one participant.
 10. A central server, comprising: a memorydevice comprising a set of instructions; a processor communicativelycoupled with the memory device, wherein the processor is configured to:identify, in real time, a trigger event initiated by at least oneparticipant of the meeting, wherein the trigger event is indicative ofat least a reference to meeting metadata associated with the meeting;record meeting data associated with the at least one participant of themeeting for a determined duration to generate a meeting snippet, whereinthe recording is based on the identified trigger, wherein the meetingsnippet is associated with the at least one participant; train a machinelearning (ML) model associated with the at least one participant basedon the meeting snippet associated with the at least one participant; andgenerate one or more meeting recommendations by utilizing the trained MLmodel, wherein the one or more meeting recommendations include meetingmetadata for another meeting.
 11. The central server of claim 10,wherein the meeting data comprises a transcript of the audio generatedby the at least one participant during the meeting, a transcript of thecontent shared by the at least one participant during the meeting,and/or meeting notes input by the at least one participant.
 12. Thecentral server of claim 10, wherein the processor is configured to trainthe ML model based on the meeting metadata associated with the meeting.13. The central server of claim 10, wherein the ML model may beconfigured to associate the at least one participant with one or moretopics.
 14. The central server of claim 10, wherein the processor isconfigured to generate the one or more meeting recommendations based onthe ML model associated with the other participants of the othermeeting.
 15. The central server of claim 10, wherein the processor isfurther configured to utilize the ML model associated with the at leastone participant to facilitate capture of the meeting data during theother meeting.
 16. The central server of claim 10, wherein the processoris further configured to retrieve task metadata associated with one ormore tasks assigned to the at least one participant, from one or moreproject management tools.
 17. The central server of claim 16, whereinthe processor is further configured to append the task metadata to themeeting data.
 18. The central server of claim 10, wherein the processoris further configured to train the ML model associated with the at leastone participant based on the meeting data associated with the at leastone participant.
 19. A non-transitory computer-readable medium havingstored thereon, computer-readable instructions, which when executed by acomputer, causes a processor in the computer to execute operations, theoperations comprising: identifying, in real time, a trigger eventinitiated by at least one participant of the meeting, wherein thetrigger event is indicative of at least a reference to meeting metadataassociated with the meeting; recording, meeting data associated with theat least one participant of the meeting for a determined duration togenerate meeting snippet, wherein the recording is based on theidentified trigger; training, a machine learning (ML) model associatedwith the at least one participant based on the meeting snippetassociated with the at least one participant; and generating, one ormore meeting recommendations by utilizing the trained ML model, whereinthe one or more meeting recommendations include meeting metadata foranother meeting.
 20. The non-transitory computer-readable medium ofclaim 19, wherein the meeting data comprises transcript of the audiogenerated by the at least one participant during the meeting, transcriptof the content shared by the at least one participant during themeeting, and/or meeting notes input by the at least one participant.